scholarly journals Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

2020 ◽  
Vol 9 (6) ◽  
pp. 1767 ◽  
Author(s):  
Charat Thongprayoon ◽  
Panupong Hansrivijit ◽  
Tarun Bathini ◽  
Saraschandra Vallabhajosyula ◽  
Poemlarp Mekraksakit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Kidney Injury ◽  
Risk Scores ◽  
Learning Approaches ◽  
Risk Prediction Models ◽  
General Utility

Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

scholarly journals Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches

2021 ◽  
Vol 18 (23) ◽  
pp. 12586
Author(s):  
Mirza Rizwan Sajid ◽  
Bader A. Almehmadi ◽  
Waqas Sami ◽  
Mansour K. Alzahrani ◽  
Noryanti Muhammad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Risk Prediction ◽  
Prediction Models ◽  
Good Choice ◽  
Support Vector ◽  
Learning Approaches ◽  
Middle Income ◽  
Risk Prediction Models ◽  
Low Middle Income Countries

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.

Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning

2021 ◽  
Author(s):  
Chenxi Huang ◽  
Shu-Xia Li ◽  
César Caraballo ◽  
Frederick A. Masoudi ◽  
John S. Rumsfeld ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Prediction Models ◽  
Learning Models ◽  
Risk Prediction Models ◽  
Clinical Risk ◽  
Machine Learning Models

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics. Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics. Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models.

scholarly journals Developing Risk Prediction Models for Kidney Injury and Assessing Incremental Value for Novel Biomarkers

2014 ◽  
Vol 9 (8) ◽  
pp. 1488-1496 ◽  
Author(s):  
Kathleen F. Kerr ◽  
Allison Meisner ◽  
Heather Thiessen-Philbrook ◽  
Steven G. Coca ◽  
Chirag R. Parikh
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Kidney Injury ◽  
Risk Prediction Models ◽  
Incremental Value ◽  
Novel Biomarkers

P3429A novel prediction model for mortality after cardiac surgery using institutional case volume

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Lee ◽  
J B Park ◽  
Y J Cho ◽  
H G Ryu ◽  
E J Jang
Keyword(s):  
Cardiac Surgery ◽  
Hospital Mortality ◽  
Risk Prediction ◽  
Prediction Models ◽  
Validation Cohort ◽  
Mortality Data ◽  
P Value ◽  
Case Volume ◽  
Risk Prediction Models ◽  
Derivation Cohort

Abstract Purpose A number of risk prediction models have been developed to identify short term mortality after cardiovascular surgery. Most models include patient characteristics, laboratory data, and type of surgery, but no consideration for the amount of surgical experience. With numerous reports on the impact of case volume on patient outcome after high risk procedures, we attempted to develop a risk prediction models for in-hospital and 1-year mortality that takes institutional case volume into account. Methods We identified adult patients who underwent cardiac surgery from January 2008 to December 2017 from the National Health Insurance Service (NHIS) database by searching for patients with procedure codes of coronary artery bypass grafting, valve surgery, and surgery on thoracic aorta during the hospitalization. Study subjects were randomly assigned to either the derivation cohort or the validation cohort. In-hospital mortality and 1-year mortality data were collected using the NHIS database. Risk prediction models were developed from the derivation cohort using Cox proportional hazards regression. The prediction performances of models were evaluated in the validation cohort. Results The models developed in this study demonstrated fair discrimination for derivation cohort (N=22,004, c-statistics, 0.75 for in-hospital mortality; 0.73 for 1-year mortality) and acceptable calibration in the validation cohort. (N=22,003, Hosmer-Lemeshow χ2-test, P=0.08 and 0.16, respectively). Case volume was the key factor of mortality prediction models after cardiac surgery. (50≤ x <100 case per year. 100≤ x <200 case per year, ≥200 case per year are correlated with OR 3.29, 2.49, 1.85 in in-hospital mortality, 2.76, 1.99, 1.69 in 1-year mortality respectively, P value <0.001.) Annual case volume as risk factor Variables In-hospital mortality 1-year mortality OR (95% CI) p-value OR (95% CI) p-value Annual case-volume (reference: ≥200) – – 100–200 1.69 (1.48, 1.93) <0.001 1.85 (1.58, 2.18) <0.001 50–100 1.99 (1.75, 2.25) <0.001 2.49 (2.15, 2.89) <0.001 <50 2.76 (2.44, 3.11) <0.001 3.29 (2.85, 3.79) <0.001 OR: Odds ratio; CI: confidence interval; Ref: Reference. Discrimination and calibration Conclusion We developed and validated new risk prediction models for in-hospital and 1-year mortality after cardiac surgery using the NHIS database. These models may provide useful guides to predict mortality risks of patients with basic information and without laboratory findings.

scholarly journals Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jae Seung Kang ◽  
Chanhee Lee ◽  
Wookyeong Song ◽  
Wonho Choo ◽  
Seungyeoun Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Risk Prediction ◽  
Prediction Models ◽  
Model Development ◽  
Test Set ◽  
Risk Prediction Models ◽  
Mucinous Neoplasms ◽  
Tumour Location ◽  
Tenfold Cross Validation

AbstractMost models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.

scholarly journals How accurate are suicide risk prediction models? Asking the right questions for clinical practice

2019 ◽  
Vol 22 (3) ◽  
pp. 125-128 ◽  
Author(s):  
Daniel Whiting ◽  
Seena Fazel
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Suicide Risk ◽  
Potential Role ◽  
Prediction Models ◽  
Learning Approaches ◽  
Risk Prediction Models ◽  
Self Harm ◽  
The Right

Prediction models assist in stratifying and quantifying an individual’s risk of developing a particular adverse outcome, and are widely used in cardiovascular and cancer medicine. Whether these approaches are accurate in predicting self-harm and suicide has been questioned. We searched for systematic reviews in the suicide risk assessment field, and identified three recent reviews that have examined current tools and models derived using machine learning approaches. In this clinical review, we present a critical appraisal of these reviews, and highlight three major limitations that are shared between them. First, structured tools are not compared with unstructured assessments routine in clinical practice. Second, they do not sufficiently consider a range of performance measures, including negative predictive value and calibration. Third, the potential role of these models as clinical adjuncts is not taken into consideration. We conclude by presenting the view that the current role of prediction models for self-harm and suicide is currently not known, and discuss some methodological issues and implications of some machine learning and other analytic techniques for clinical utility.

Sign in / Sign up

Export Citation Format

Share Document